Cluster homogeneity as a semi-supervised principle for feature selection using mutual information

Coelho, Frederico;Braga, Antonio Padua;Verleysen, Michel
(2012) 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012) — Location: Bruges (Belgium) (25.April.2012)

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Authors
  • Coelho, FredericoUniversidade Federal de Minas Gerais, Brazil
    Author
  • Braga, Antonio PaduaUniversidade Federal de Minas Gerais, Brazil
    Author
  • Verleysen, MichelUCLouvain
    Author
Abstract
In this work the principle of homogeneity between labels and data clusters is exploited in order to develop a semi-supervised Feature Selection method. This principle permits the use of cluster information to improve the estimation of feature relevance in order to increase selection performance. Mutual Information is used in a Forward-Backward search process in order to evaluate the relevance of each feature to the data distribution and the existent labels, in a context of few labeled and many unlabeled instances.
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Coelho, F., Braga, A. P., & Verleysen, M. (2012). Cluster homogeneity as a semi-supervised principle for feature selection using mutual information. Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), p. 507-512. https://hdl.handle.net/2078.5/253741